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題名：多目標塑膠射出成形之製程參數最佳化系統
作者：傅公良
作者(外文)：FU,Gong-Loung
校院名稱：中華大學
系所名稱：科技管理學系(所)
指導教授：陳 文 欽
王 珉 玟
學位類別：博士
出版日期：2010
主題關鍵詞：反應曲面法；田口方法；類神經網路法；基因演算法；模擬退火法；粒子族群演算法；response surface methodology；Taguchi method；back-propagation neural network；genetic algorithm；simulated annealing；particle swarm optimization
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塑膠射出成形技術已廣泛地應用於高科技產業，由於塑膠製品多樣化及輕薄短小的趨勢，且希望能有更精準的控制，更快速的反應，以生產高精度、高利潤之產品，而如何有效掌握產品品質，是攸關量產良率之主因。射出成形系統整體製程複雜，射出成形機之成形參數及其系統控制模式大多為非線性而難以求得最適解，且隨著未知參數的增加，其複雜度將是以幾何級數呈現大幅度變化。
在以往產品品質之衡量通常以單一品質特性目標做衡量，就算是多品質特性，各品質指標間衡量往往是互相獨立的，由於產品之品質通常不只衡量一個品質特性，且各品質特性間多具有關聯性，因此要將目前製程高度自動化的產品品質提升，其中設備的使用率、機台精密度、環境與設備的操控者之間的變數等，對產品品質都具有相當大的影響。但是在現有的射出成形的控制架構中，不論是機台的特性差異或外在雜訊的影響，對於受控系統均有不確定的干擾，因此在實際環境的應用上，CAE 軟體所分析得到的控制參數並不完全適合直接應用於機台上，因此亟需設計ㄧ具有智慧成形之參數最佳化系統。
本研究提出建構多目標塑膠射出成形之三階段製程參數最佳化系統，第一、二階段以CAE 軟體執行DOE 因子篩選模擬實驗及結合RSM-GA 參數優化所得之製程參數最佳設定，提供第三階段實際射出製程參數控制之科學依據。本文CAE 軟體是利用Moldex3D 模流分析軟體，搭配Rhinoceros 4.0 為建模平台來製作網格(mesh)，產生解析度足夠的網格；並由電腦模擬分析結果達到開模前預測產品可能形成之缺陷，減少產品研發的週期(cycle time)，增加可靠度，可有效提升開模效率及降低試誤成本。第三階段進行了L25 田口實驗將實際射出製程參數優化- 透過實驗計算出訊號雜音比(S/N Ratio)及靈敏度並應用ANOVA 分析，找出實驗顯著因子之最初製程參數組合，另將實驗數據資料經由倒傳遞神經網路(Back-Propagation Neural Network)訓練與測試，建構S/N 比預測器，輔以S/N 比預測器搭配模擬退火法(Simulated Annealing,SA)，進行製程參數S/N 比最佳化，使製程變異數最小達至穩定狀態，最後針對品質特性進行ANOVA 分析及倒傳遞神經網路訓練與測試建構品質預測器，以品質預測器搭配粒子群最佳化演算法(Particle Swarm Optimization, PSO)，進行製程參數多品質最佳化，更進一步逼近產品所要求之多品質目標值規範，使製程不僅穩定，也同時符合產品多品質規範。因此本研究主要目的是運用CAE 模擬與智慧成形參數之最佳化方法，針對射出成形產品製程參數作整合性研究，進而達到優化模具及有效降低成本的目標。
The plastic injection molding (PIM) has been widely researched and routinely applied in many high-tech industries; the accuracy and precision of this technology are being severely scrutinized in the wake of the evolutionary trend of making products diverse and creative. In this regard, how to effectively grasp the best and suitable quality of products is always the crucial issue associated with the throughput and the yield. By virtue of the underlying plastic injection molding system causing the process parameter settings in injection molding machines to be complicated, the nonlinear control model for the injection molding system is hard to be obtained with exponentially changed complexity while more unknown parameters are added.
In the past, PIM product quality was usually measured by one single quality
characteristic or by multiple quality characteristics with independent parameters one another. With the increasing complexity of product, this dissertation proposes a three-stage integrated optimization system to generate the optimal process parameter settings of multiple-quality characteristics. In the first stage, the significant PIM process parameters can be determined by DOE screening experiments. In the second stage, the optimal process parameter settings are obtained via computer aided engineering (CAE) simulation integrated with response surface methodology (RSM) and genetic algorithm (GA) , which are taken as practically initial settings of process-related parameters. As for the previous two stages, the mold-flow analysis software (i.e., Moldex3D and Rhinoceros 4.0) is used as a working platform to develop mesh with a sufficient resolution, and predict possible defects observed on one product by way of a computer’s simulated results for reduced cycle time, increased reliability, improved efficiency in die sinking, and decreased cost from CAE simulation instead of trial-and-error process prior to die sinking.
In the final stage, Taguchi method and back-propagation neural network (BPNN) are utilized for developing a signal-to-noise (S/N) ratio predictor and executing analysis of variance (ANOVA) to analyze the factors’ significance effects of initial process-related parameter settings, then the BPNN S/N ratio predictor conducting with simulated annealing (SA) to optimize the process-related parameter settings for S/N ratios characteristic and minimize/stabilize variations of one process; moreover, the BPNN quality predictor also works in with particle swarm optimization (PSO) for multi-quality/ multi-objective characteristics (i.e., length and warpage) optimization of process-related process parameters.
In addition, the above-mentioned CAE simulations and intelligent integrated process parameter optimization system are employed to search the optimal PIM process parameters,and can further obtain the optimal die design and effectively reduce the costs.
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